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Found 4 papers, 2 papers with code
Lidar-Camera Co-Training for Semi-Supervised Road Detection
Recent advances in the field of machine learning and computer vision have enabled the development of fast and accurate road detectors.
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches.
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
The fully convolutional neural network trained using all the available sensors together with driving directions achieved the best MaxF score of 88. 13% when considering a region of interest of 60x60 meters.
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps.
